For depth estimation on NYUv2 and KITTI, we use the standard splits and report the RMSE (Root Mean Square Error) as the evaluation metric.
To compare with the previous works [1]}, [2]}, we set the maximum range as 10m/80m for NYUv2/KITTI.
The head of the depth estimation is the same as that of the pose estimation and is comprised of deconvolutions.
Similar to the GLPDepth [2]}, we use the following data augmentations: random horizontal flip, random brightness/gamma/hue/saturation/value and random vertical CutDepth. We randomly crop the images to \(480\times 480\)  / \(352\times 352\)  size for NYUv2/KITTI dataset. The optimizer, layer decay, and DropPath is the same as the pose estimation. The learning rate is scheduled via polynomial strategy with a factor of \(0.9\)  with a minimal value of \(3e\) -5 and a maximum value of \(5e\) -4. The total number of epochs is 25. We use the flip testing and sliding window test.
